Identification of immune-related gene signatures to evaluate immunotherapeutic response in cancer patients using exploratory subgroup discovery

Phenotypic and genotypic heterogeneity are characteristic features of cancer patients. To tackle patients[trademark] heterogeneity, immune checkpoint inhibitors (ICIs) represent one of the most promising therapeutic approaches. However, approximately 50 percent of cancer patients that are eligible for treatment with ICIs will not respond well, which motivates the exploration of immunotherapy in combination with either targeted treatments or chemotherapy. Over the years, multiple patient stratification techniques have been developed to identify homogenous patient subgroups, although, matching patient subgroup to treatment option that can improve patients[trademark] health outcome remains a challenging task. We extend our exploratory subgroup discovery algorithm to identify patient subpopulations that can potentially benefit from immuno-targeted combination therapies or chemoimmunotherapy in five cancer types: Head and Neck Squamous Carcinoma (HNSC), Lung Adenocarcinoma (LUAD), Lung Squamous Carcinoma (LUSC), Skin Cutaneous Melanoma (SKCM) and Triple-Negative Breast Cancer (TNBC). We employ various regression models to identify immune-related gene signatures and drug targets that increase the likelihood of partial remission on combination therapies, either immunotargeted regimen or chemoimmunotherapy. Moreover, our pipelines can pinpoint adverse drug effects associated with predicted drug combinations. In addition, we uncovered distinct immune cell populations (T-cells, B-cells, Myeloid, NK-cells) for TNBC patients that differentiate patients with partial remission from patients with progressive disease after chemoimmunotherapy. Finally, we incorporate our methodological developments on Mutational Forks Formalism that enable an assessment of patient-specific flow by leveraging information from multiple single-nucleotide alterations to adjust the transitional likelihoods that are solely based on the canonical view of a disease. Our suit of methods can help to better select responders for combination therapies and improve health outcome for cancer patients with limited treatment options.Includes bibliographical references

Metodi statistici per la stima di profili di rischio personalizzati basati sulla medicina di precisione del cancro nei pazienti oncologici

Precision medicine is beginning to emerge as a well-defined discipline with specific goals, areas of focus, and tailored methodology. Specifically, the primary goal is to discover treatment rules that leverage heterogeneity to improve clinical decision making in a manner that is reproducible, generalizable, and adaptable as needed. This endeavor spans a broad range of scientific areas including drug discovery, genetics/genomics, health communication, and causal inference, all in support of evidence-based, i.e., data-driven, decision making. Precision Medicine allows patients to be discriminated according to their level of risk (e.g. low or high) and identifies subgroups of patients according to their characteristics in order to assign the treatment to those who are likely to benefit. Statistics research in precision medicine is broadly focused on methodological development for estimation of and inference for treatment regimens that maximize some cumulative clinical outcome. The process for using statistical inference to establish personalized treatment strategies requires specific techniques for data-analysis that optimize the combination of competing therapies with candidate genetic features and characteristics of the patient and disease. The present dissertation focuses on the implementation and application of statistical methods for establishing optimal treatment rules for personalized medicine and discuss specific examples in various medical contexts with oncology as an emphasis. I have focused my research activity mainly in the study of the following topics. 1) Statistical methods to analyze continuous biomarkers. Several approaches were considered according to the design of study: from classical approach - median or mean value, percentiles, optimal cut-point identified by means standard receiver operating characteristic (ROC) analysis-to more complex analysis - time-dependent ROC, conditional inferential tree and subpopulation Treatment Effect Pattern (STEPP) method. 2) Statistical methods for time-to-event endpoints. Competing risks occur commonly in medical research. In the analysis of competing risks data, methods of standard survival analysis lead to incorrect and biased results. In the presence of competing risks, data analysis has to be performed including methods to calculate the cumulative incidence of an event of interest, to compare cumulative incidence curves in the presence of competing risks, and to perform competing risks regression analysis. 3) Meta-analysis for synthesizing evidence. 4) An important topic reviews to use of several statistical methods that handle the issue of treatment switching. The contribution aims at assessing tamoxifen treatment effect taking into account treatment switches, in order to provide a robust assessment of treatment effect applying causal inference methods. 5) The last topic deals with the use of population-based registry and administrative databases. The objective of this project is to develop an acceptable claims-based algorithm to identify second breast cancer events during a 10-year follow-up through a record-linkage of two data sources:the Friuli Venezia Giulia population based-cancer registry and the administrative individual-record FVG database.Precision medicine is beginning to emerge as a well-defined discipline with specific goals, areas of focus, and tailored methodology. Specifically, the primary goal is to discover treatment rules that leverage heterogeneity to improve clinical decision making in a manner that is reproducible, generalizable, and adaptable as needed. This endeavor spans a broad range of scientific areas including drug discovery, genetics/genomics, health communication, and causal inference, all in support of evidence-based, i.e., data-driven, decision making. Precision Medicine allows patients to be discriminated according to their level of risk (e.g. low or high) and identifies subgroups of patients according to their characteristics in order to assign the treatment to those who are likely to benefit. Statistics research in precision medicine is broadly focused on methodological development for estimation of and inference for treatment regimens that maximize some cumulative clinical outcome. The process for using statistical inference to establish personalized treatment strategies requires specific techniques for data-analysis that optimize the combination of competing therapies with candidate genetic features and characteristics of the patient and disease. The present dissertation focuses on the implementation and application of statistical methods for establishing optimal treatment rules for personalized medicine and discuss specific examples in various medical contexts with oncology as an emphasis. I have focused my research activity mainly in the study of the following topics. 1) Statistical methods to analyze continuous biomarkers. Several approaches were considered according to the design of study: from classical approach - median or mean value, percentiles, optimal cut-point identified by means standard receiver operating characteristic (ROC) analysis-to more complex analysis - time-dependent ROC, conditional inferential tree and subpopulation Treatment Effect Pattern (STEPP) method. 2) Statistical methods for time-to-event endpoints. Competing risks occur commonly in medical research. In the analysis of competing risks data, methods of standard survival analysis lead to incorrect and biased results. In the presence of competing risks, data analysis has to be performed including methods to calculate the cumulative incidence of an event of interest, to compare cumulative incidence curves in the presence of competing risks, and to perform competing risks regression analysis. 3) Meta-analysis for synthesizing evidence. 4) An important topic reviews to use of several statistical methods that handle the issue of treatment switching. The contribution aims at assessing tamoxifen treatment effect taking into account treatment switches, in order to provide a robust assessment of treatment effect applying causal inference methods. 5) The last topic deals with the use of population-based registry and administrative databases. The objective of this project is to develop an acceptable claims-based algorithm to identify second breast cancer events during a 10-year follow-up through a record-linkage of two data sources:the Friuli Venezia Giulia population based-cancer registry and the administrative individual-record FVG database

Bayesian nonparametric models for data exploration

Mención Internacional en el título de doctorMaking sense out of data is one of the biggest challenges of our time. With the emergence of technologies such as the Internet, sensor networks or deep genome sequencing, a true data explosion has been unleashed that affects all fields of science and our everyday life. Recent breakthroughs, such as self-driven cars or champion-level Go player programs, have demonstrated the potential benefits from exploiting data, mostly in well-defined supervised tasks. However, we have barely started to actually explore and truly understand data. In fact, data holds valuable information for answering most important questions for humanity: How does aging impact our physical capabilities? What are the underlying mechanisms of cancer? Which factors make countries wealthier than others? Most of these questions cannot be stated as well-defined supervised problems, and might benefit enormously from multidisciplinary research efforts involving easy-to-interpret models and rigorous data exploratory analyses. Efficient data exploration might lead to life-changing scientific discoveries, which can later be turned into a more impactful exploitation phase, to put forward more informed policy recommendations, decision-making systems, medical protocols or improved models for highly accurate predictions. This thesis proposes tailored Bayesian nonparametric (BNP) models to solve specific data exploratory tasks across different scientific areas including sport sciences, cancer research, and economics. We resort to BNP approaches to facilitate the discovery of unexpected hidden patterns within data. BNP models place a prior distribution over an infinite-dimensional parameter space, which makes them particularly useful in probabilistic models where the number of hidden parameters is unknown a priori. Under this prior distribution, the posterior distribution of the hidden parameters given the data will assign high probability mass to those configurations that best explain the observations. Hence, inference over the hidden variables can be performed using standard Bayesian inference techniques, therefore avoiding expensive model selection steps. This thesis is application-focused and highly multidisciplinary. More precisely, we propose an automatic grading system for sportive competitions to compare athletic performance regardless of age, gender and environmental aspects; we develop BNP models to perform genetic association and biomarker discovery in cancer research, either using genetic information and Electronic Health Records or clinical trial data; finally, we present a flexible infinite latent factor model of international trade data to understand the underlying economic structure of countries and their evolution over time.Uno de los principales desafíos de nuestro tiempo es encontrar sentido dentro de los datos. Con la aparición de tecnologías como Internet, redes de sensores, o métodos de secuenciación profunda del genoma, una verdadera explosión digital se ha visto desencadenada, afectando todos los campos científicos, así como nuestra vida diaria. Logros recientes como pueden ser los coches auto-dirigidos o programas que ganan a los seres humanos al milenario juego del Go, han demostrado con creces los posibles beneficios que podemos obtener de la explotación de datos, mayoritariamente en tareas supervisadas bien definidas. No obstante, apenas hemos empezado con la exploración de datos y su verdadero entendimiento. En verdad, los datos encierran información muy valiosa para responder a muchas de las preguntas más importantes para la humanidad: ¿Cómo afecta el envejecimiento a nuestras aptitudes físicas? ¿Cuáles son los mecanismos subyacentes del cáncer? ¿Qué factores explican la riqueza de ciertos países frente a otros? Si bien la mayoría de estas preguntas no pueden formularse como problemas supervisados bien definidos, éstas pueden ser abordadas mediante esfuerzos de investigación multidisciplinar que involucren modelos fáciles de interpretar y análisis exploratorios rigurosos. Explorar los datos de manera eficiente abre potencialmente la puerta a un sinnúmero de descubrimientos científicos en diversas áreas con impacto real en nuestras vidas, descubrimientos que a su vez pueden llevarnos a una mejor explotación de los datos, resultando en recomendaciones políticas adecuadas, sistemas precisos de toma de decisión, protocolos médicos optimizados o modelos con mejores capacidades predictivas. Esta tesis propone modelos Bayesianos no-paramétricos (BNP) adecuados para la resolución específica de tareas explorativas de los datos en diversos ámbitos científicos incluyendo ciencias del deporte, investigación contra el cáncer, o economía. Recurrimos a un planteamiento BNP para facilitar el descubrimiento de patrones ocultos inesperados subyacentes en los datos. Los modelos BNP definen una distribución a priori sobre un espacio de parámetros de dimensión infinita, lo cual los hace especialmente atractivos para enfoques probabilísticos donde el número de parámetros latentes es en principio desconocido. Bajo dicha distribución a priori, la distribución a posteriori de los parámetros ocultos dados los datos asignará mayor probabilidad a aquellas configuraciones que mejor explican las observaciones. De esta manera, la inferencia sobre el espacio de variables ocultas puede realizarse mediante técnicas estándar de inferencia Bayesiana, evitando el proceso de selección de modelos. Esta tesis se centra en el ámbito de las aplicaciones, y es de naturaleza multidisciplinar. En concreto, proponemos un sistema de gradación automática para comparar el rendimiento deportivo de atletas independientemente de su edad o género, así como de otros factores del entorno. Desarrollamos modelos BNP para descubrir asociaciones genéticas y biomarcadores dentro de la investigación contra el cáncer, ya sea contrastando información genética con la historia clínica electrónica de los pacientes, o utilizando datos de ensayos clínicos; finalmente, presentamos un modelo flexible de factores latentes infinito para datos de comercio internacional, con el objetivo de entender la estructura económica de los distintos países y su correspondiente evolución a lo largo del tiempo.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Joaquín Míguez Arenas.- Secretario: Daniel Hernández Lobato.- Vocal: Cédric Archambea

Convergence, connectivity, and continuity: topological perspectives for mining novel biological information from ‘omics data

In this thesis, we will explore possible applications of topological data analysis to `omics data. More specifically, we apply the topologically-based data visualisation technique, Mapper, to gene expression data coming from the fish, Arctic charr (\textit{Salvelinus alpinus}). The fish samples come from the wild, from lakes in Scotland and Russia. Furthermore, the Arctic charr is an interesting study species, since it commonly occurs in two morphs, a bottom/bank-dwelling benthic morph, and an open-water pelagic morph. In general, these morphs share features which are common across lakes, and so provide an opportunity to study a subspecies-level split which is replicated across different populations. This gives an example of parallelism in evolution, and the fact that the split is replicated allows us to test if there are common underlying changes leading to this split, at the level of identical genes, or sets of genes, or genes involved in the same pathways. We provide an overview of the Mapper algorithm, and also show its application to a breast cancer gene expression dataset, which was the inspiration for our PhD project. When applying Mapper to the Arctic charr, we also investigate the effect of sample size by subsampling the breast cancer data. As well as applying Mapper, we also use a more mathematical view of the gene expression data to provide a new perspective for looking at the commonly used gene analysis techniques in evolutionary biology, namely, differential gene expression, and gene co-expression analysis. Finally, we provide an experiment which could be done in the future, assuming the cost of sequencing continues to fall. This experiment incorporates ideas of optimal transport in trying to reconstruct the developmental landscape of Arctic charr. We also discuss other avenues for future work, and current difficulties with applying topological data analysis to gene expression data from wild samples

Histološka različitost u koštanoj biopsiji postmenopauzalnih i osteoporotičnih žena

Many investigations have been carried out on osteoporosis and showed this disorder in a new light. The reduction in bone structure and dynamics points to the etiology that may be of therapeutic significance. Today we still do not know everything about the histomorphometric characteristics of each metabolic disturbance and bone disease. Bone biopsy and histomorphometry are not available for most patients and that is the reason why a broad classification of these reductions in osteoporosis and osteomalacia are widely accepted in clinical practice. New findings from this complex domain are necessary to design a strategy in the evolution of new therapeutic devices and drugs with the intention to decrease the high disability and mortality rate in the vulnerable population of postmenopausal women.Brojna istraživanja provedena posljednjih godina pokazala su osteoporozu u novom svjetlu. Promjene u koštanoj strukturi i dinamici upućuju na etiologiju poremećaja, što je od presudnog značenja za liječenje bolesti. S obzirom na činjenicu da još uvijek malo znamo o histomorfometrijskim značajkama pojedinih metaboličnih bolesti te da je biopsija kosti i histomorfometrija još uvijek teže dostupna većem broju bolesnika, danas se u kliničkoj praksi uglavnom rabi klinička klasifikacija po kojoj se one dijele u dvije široke kategorije: osteoporozu i osteomalaciju. Nova saznanja iz ovoga područja su od presudne važnosti za planiranje buduće strategije razvoja novih terapijskih sredstava i lijekova kako bi se smanjio visok postotak invaliditeta i smrtnosti u osjetljivoj populaciji postmenopauzalnih žena